Clustering algorithms | Dataset structure | Description |
DBSCAN [19] |
| Grouped Hard Clustering No overlapping data attributes Micro-cluster is considered as a different group is minimum density value reached A data that not reached the minimum density, will treat as noise, a triangle also a shape, but it considered as noise |
Expectation Maximization, EM [20] |
| Tree Structured Soft Clustering A data could be a member of many clusters Micro-cluster is considered as a different group or to be placed in a greater group |
Fuzzy C Means, FCM [23] |
| Grouped Soft Clustering A data could be member of many clusters based on degree of membership, normally the border point data |
HDBSCAN [21] |
| Tree Structured Hard Clustering No overlapping data attributes Subclustered is not an overlapping cluster, it just placed under a greater cluster |
K-MEANS [22] |
| Grouped Hard Clustering No overlapping data attributes No data will be treated as outliers Must be have an exact number of clusters need to be form, otherwise the triangle will be the member of rectangle or round |